Question Answering

The Question Answering task in NLP pertains to building a model which can answer questions posed in natural language. Many datasets (including SQuAD, the dataset we use in this notebook) pose this as a reading comprehension task i.e. given a question and a context, the goal is to predict the span within the context with a start and end position which indicates the answer to the question. For every word in the training dataset we predict:

likelihood this word is the start of the span
likelihood this word is the end of the span

The best place to get started with TAO Toolkit - Question Answering would be the TAO - Question Answering jupyter notebooks. This resource has two notebooks included.

Training: Sample workflow for training a question answering model and export the model to a .riva file
Deployment: Sample workflow to consume the .riva file and deploy it to Riva.

If you are a seasoned Conversation AI developer we recommend installing TAO and referring to the TAO documentation for usage information.

Pre-Requisites

Please make sure to install the following before proceeding further:

python 3.6.9
docker-ce > 19.03.5
docker-API 1.40
nvidia-container-toolkit > 1.3.0-1
nvidia-container-runtime > 3.4.0-1
nvidia-docker2 > 2.5.0-1
nvidia-driver >= 455.23

Note: A compatible NVIDIA GPU would be required.

Installation

We recommend that you install TAO Toolkit inside a virtual environment. The steps to do the same are as follows.

virtualenv -p python3 \nsource /bin/activate\npip install jupyter notebook # If you need to run the notebooks\n

TAO Toolkit is a python package that is hosted in nvidia python package index. You may install by using python’s package manager, pip.

pip install nvidia-pyindex\npip install nvidia-tao\n

To download the jupyter notebook please:

Download the samples using the ngc cli with the following command

ngc registry resource download-version \"nvidia/tao/questionanswering_notebook:v1.0\"\n

Instantiate the jupyter notebook server

jupyter notebook --ip 0.0.0.0 --allow-root --port 8888\n

License

By downloading and using the models and resources packaged with TAO Toolkit Conversational AI, you would be accepting the terms of the Riva license

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Many datasets (including SQuAD, the dataset we use in this notebook) pose this as a reading comprehension task i.e. given a question and a context, the goal is to predict the span within the context with a start and end position which indicates the answer to the question. For every word in the training dataset we predict:\n\n* likelihood this word is the start of the span\n* likelihood this word is the end of the span\n\nThe best place to get started with TAO Toolkit - Question Answering would be the TAO - Question Answering jupyter notebooks. This resource has two notebooks included.\n\n1. Training: Sample workflow for training a question answering model and export the model to a `.riva` file\n2. Deployment: Sample workflow to consume the `.riva` file and deploy it to Riva.\n\nIf you are a seasoned Conversation AI developer we recommend installing TAO and referring to the [TAO documentation](https://docs.nvidia.com/tao/tao-toolkit/text/overview.html) for usage information.\n\n## Pre-Requisites\n\nPlease make sure to install the following before proceeding further:\n\n* python 3.6.9\n* docker-ce > 19.03.5\n* docker-API 1.40\n* nvidia-container-toolkit > 1.3.0-1\n* nvidia-container-runtime > 3.4.0-1\n* nvidia-docker2 > 2.5.0-1\n* nvidia-driver >= 455.23\n\n*Note: A compatible NVIDIA GPU would be required.*\n\n## Installation\n\nWe recommend that you install TAO Toolkit inside a virtual environment. The steps to do the same are as follows.\n\n```sh\nvirtualenv -p python3 \nsource /bin/activate\npip install jupyter notebook # If you need to run the notebooks\n```\n\nTAO Toolkit is a python package that is hosted in nvidia python package index. You may install by using python’s package manager, pip.\n\n```sh\npip install nvidia-pyindex\npip install nvidia-tao\n```\n\nTo download the jupyter notebook please:\n\n1. Download the samples using the ngc cli with the following command\n\n```sh\nngc registry resource download-version \"nvidia/tao/questionanswering_notebook:v1.0\"\n```\n\n2. Instantiate the jupyter notebook server\n\n```sh\njupyter notebook --ip 0.0.0.0 --allow-root --port 8888\n```\n\n## License\n\nBy downloading and using the models and resources packaged with TAO Toolkit Conversational AI, you would be accepting the terms of the Riva [license](https://developer.nvidia.com/riva/ga/license)","displayName","Question Answering Notebook",["D",1680632423836],"publisher","NVIDIA","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/tao/resources/questionanswering_notebook","rawImageUrl","https://dz112fgwz7ogh.cloudfront.net/logos/Nvidia-Centric-TAO-RIVA.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[277,278,279,280,281,282,283],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_246":304,"_285":304,"_286":305,"_288":88},{"_246":302,"_285":302,"_286":303,"_288":88},{"_246":298,"_285":299,"_286":300,"_288":88},{"_246":294,"_285":295,"_286":296,"_288":88},{"_246":292,"_285":292,"_286":293,"_288":88},{"_246":290,"_285":290,"_286":291,"_288":88},{"_246":284,"_285":284,"_286":287,"_288":88},"Transfer Learning","value","filter",{"_289":88,"_285":284},"category","field","QuestionAnswering",{"_289":88,"_285":290},"QA",{"_289":88,"_285":292},"Natural Language Understanding","uscs_natural_language_understanding",{"_289":297,"_285":295},"application","Inference","soln_inference",{"_289":301,"_285":299},"technology","Finetuning",{"_289":88,"_285":302},"BERT",{"_289":88,"_285":304},{"_159":1283,"_161":1284},"ngcApps",[1236],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1217":1218,"_1219":1220,"_1221":1222,"_1223":1224,"_1225":317,"_110":343,"_1226":1227,"_1228":60,"_1229":91,"_1230":91,"_1231":1232,"_1233":91,"_1234":1235},"amplitude",{"_1214":1215,"_857":-7,"_1216":35},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMiUyMiUzQWZhbHNlJTdE","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1208":1209,"_1210":1211,"_1212":1213},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1787632091240,"1.12.0","defaultSearchSuggestions",{"_1097":424,"_1098":1099,"_1100":1101,"_1102":1103},"announcements",[],"consentState",{"_381":1087,"_1088":91,"_1089":35,"_1090":35,"_1091":1092},"consentConfig",{"_363":364,"_365":366,"_367":368,"_369":370,"_371":372,"_373":374,"_375":376,"_377":378,"_379":380,"_381":382},"showContactAdmin",1787632091241,[],[],[357,358,359,360,361,362],{"_49":63,"_51":74,"_53":154,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":70,"_53":154,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":68,"_53":154,"_55":-5,"_57":-5,"_59":60},{"_49":63,"_51":64,"_53":154,"_55":-5,"_57":-5,"_59":60},{"_49":61,"_51":62,"_53":154,"_55":-5,"_57":-5,"_59":60},{"_49":50,"_51":52,"_53":154,"_55":-5,"_57":-5,"_59":60},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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	Files	Compressed Size		Download
v1.3	2	46.42 KB	November 23, 2021 UTC
v1.2Selected	2	46.21 KB	August 20, 2021 UTC